Category Archives: Data Analysis

Why Your Organisation Needs Big Data


Consumer product companies are using it to predict customer behaviour, preferences, and product perception. Advertising and marketing agencies are using it to understand responsiveness to campaigns, promotions, and other advertising mediums. Manufacturers are using to it to predict the optimal time for replacement or maintenance of their products and to trace after-market support issues. Insurance companies are using it to see distinguish between the home insurance applications that can be immediately processed, and the ones that need a validating visit from an agent.

The ‘it’ here is Big Data. What is Big Data, though? Simply put, Big Data is the mining and processing of petabytes of information to gain insights into consumer behavior, promotion success, distribution efficiency, and a multitude of other aspects of business performance. An important question that may arise is whether such advanced analytics capability is worth the investment. In other words, does your organisation really need Big Data? Indeed, it does. Here’s why:

Understanding Target Audience

Big Data analysis allows Wal-Mart to better predict what products will sell and car insurance companies to understand how safely their customers actually drive. Even politician are making heavy use of Big Data and analytics to optimize their election campaigns – be it Obama in the US in 2012 or Narendra Modi in India in 2014.

Understanding the target audience and their preferences and buying patterns is one of the major areas where Big Data is being leveraged in a big way today. Companies are expanding their field of analysis by combining their traditional data sets with social media data, browser logs, text analytics, and sensor data to get a fuller picture of their customers. The most obvious and important objective is, of course, to create predictive models for successful business performance. Therefore, if you want to better the chances of your products and services succeeding with your clientele, your organisation needs a Big Data plan.

Smarter Strategizing:

The Harvard Business Review reports that the New York City Police Department makes use of Big Data Analysis “to geolocate and analyze ‘historical arrest patterns’ while cross-tab­bing them with sporting events, paydays, rainfall, traffic flows, and federal holidays to identify what NYPD calls likely crime ‘hot spots.’” Apparently, “such insight can help deploy officers to locations where crimes are likely to occur before they are actually committed.”

Indeed, the days of going with your gut alone are long gone – even for the police force, arguably one of the most instinct-driven of organizations. Today, technology makes it possible for the NYPD and other police departments to accurately anticipate and identify crime for swifter action. The same logic can be applied in a number of other fields – and Big Data is today being lever­aged in many indus­tries with impressive results. Financial institutions are using data mined from customer interactions to categorise their users into finely tuned segments that enable them to create increasingly relevant offers, web-based businesses are developing information products that combine data gathered from customers to offer more appealing recommendations and coupon programs, sports teams are using data for tracking team strategies and ticket sales, and so on.

This does not suggest that the role of human intuition, emotion, and reason in business have all been rendered redundant. But it does imply that Big Data is creating a context in which organisations can operate more efficiently. A well crafted and meticulously executed Big Data and analytics strategy makes your organization smarter.

Optimizing business processes:

Based on predictions generated from social media data, web search trends, and weather forecasts, retailers are now able to optimise their stock. HR businesses are increasingly optimizing their talent acquisition methods, as well as measurement of company culture and staff engagement using big data tools. Big Data is also being effectively tapped for supply chain optimization. Here, geographic positioning and radio frequency identification sensors are used to track goods or delivery vehicles and optimise routes by integrating live traffic data, driving conditions, etc.

Thus, Big Data is being increasingly used to optimise business processes – for organisations big and small. It can similarly help your organisation function at an optimal level.

Enabling cross-channel conversations with customers:

For most business orga­ni­za­tions today, it is all but impossible to maintain the kind of quality conversation with customers that laid the foundation for consumer trust in the yesteryears. The volume of queries, comments, and feedback coming in from various sources is simply too massive. This is where tech­ni­cal infra­struc­ture to sup­port dynamic, cross-channel con­ver­sa­tions with customers becomes absolutely nec­es­sary for orga­ni­sa­tional impact. Investing in a sound Big Data plan lets you stay close to your customers by providing you with customer insight.

McKinsey&Company’s iConsumer research suggests that “behavioral and transactional data from web usage, in-person observational research, and interviews can be analyzed to help companies make the right investments, whether based upon global, market, and customer segment level trends, or discreet consumer usage, buying factors, and attitudes.”

Preparing your orga­ni­za­tion for reversals:

The iConsumer report accounts for why organisations need to make structural changes related to Big Data. Reversals, as has been interestingly noted, “come gradually until they come suddenly.” For instance, the news­pa­per indus­try moved from boom­ing to near obso­lete within a decade of the advent of online pub­lish­ing. Another rever­sal was seen in the recording indus­try, which saw CD sales go from booming to obsolete with the advent of dig­i­tal music – again within a decade. Both these reversals were initially grad­ual and non-alarming, but they suddenly became massive in their impact.

Industry analysts and media experts anticipate the dig­i­ti­za­tion of all customer-facing orga­ni­za­tional sys­tems (ser­vice, sales,marketing) as the next major reversal. Thus, from the smallest local chains to the largest multinational companies – organisations that resist a systematic approach to data analysis, online marketing, digital distribution, etc. run the risk of becoming obsolete when the reversal comes suddenly. Adopting Big Data while we are still in the gradual phase of the reversal will see your organisation through the reversal.

Big Data is indeed the next big thing in business. Is your organisation equipped to keep up?

Seven Simple ways to be Smarter at Reading Data


While sophisticated statistical methods for data-analysis are the current focus of many business discussions, most practicing managers neither have the expertise for such analysis nor the time to learn the methods. And while their data-handing capacities remain limited, the data itself are growing by leaps and bounds. It is tough to handle the pressures of day to day operations as well as make highly informed, data-driven decisions.

Based on my experience in the data-intensive Telecom industry, here are some principles on data analysis which can help you get more insights with no extra training.

1. Keep an Open Mind

This means doubting – ‘good sense’ – which is simply a habit of thinking in a given context. Make it a practice to doubt everything you hear about the business – even from veterans. Articulate the common assumptions and subject them to scrutiny of hard data. Sometimes, they may hold true. But when they don’t, you will end up with useful insights.

2. Predictions First

Before looking at the numbers, make predictions based on your current understanding. Create as many hypotheses as possible. This makes you think rigorously about your ‘world-view’ and uncovers your assumptions about how the business parameters are related to each other. Testing these against the data will will help in sharpening your intuition in the given business environment.

3. Benchmark in the Beginning

Once confronted with a data set, begin by identifying simple benchmarks and descriptive values. Namely, minimum, maximum, and the average. Listing them out separately will help in creating a general ‘sense’ of the data, and help in identifying outliers – which are another source of business insight.

4. Compare, Compare, Compare

The division operator(/) is the magic wand for revealing insights – it essentially means comparing different fields. List out all metrics for your business. Some of these will be primitive – like ‘Total Customers’. Others will be ratios – like ‘Number of Customers per Month’. Your task is to come up with new ratios – compare every parameter with every other parameter. For example, ‘Sales per Salesman’ or ‘Customers per Region’. This can be a huge time consuming exercise – and it is also the most productive.

5. Beware of Averages and Summaries

While they are important to begin with, do not be satisfied with just averages. It is crucial to look at the distribution. Remember the 80-20 rule? Check out how does it apply to your business. Maybe it’s seventy-thirty, or ninety-ten. This helps in identifying the areas you need to focus on, as well as other business trends which are affecting the distribution.

6. Watch out for Spikes – especially the Good Ones

A good principle to follow here is – “If it is too good to be true, it is not true”. Our general tendency makes us feel good when we see great numbers on a parameter, and thus we ignore the details. However, it helps to look under the hood here too.

7. Data and Human Behavior

Beware of the idea that a huge amount of data can tell you all the stories. If you have no context, it will be more than useless – the interpretation will be error-prone. Talk to the people who have been reading the same data for some time to understand the hidden hints, quiz them on the outliers to deepen your comprehension. This will make you better at understanding the behavioral traits underpinning the numbers.

After all, business is more about people, and less about data.

What techniques have you discovered to learn more from your data? How do you reach uncommon insights? Looking forward to your comments.